Mapping the Changes in Urban Greenness Based on Localized Spatial Association Analysis under Temporal Context Using MODIS Data

Vegetation plays an irreplaceable role for urban ecosystem services. Urban greenness represents all vegetation cover in and around cities. Understanding spatiotemporal patterns of the changes in urban greenness (CUG) provides fundamental clues for urban planning. The impact on CUG can be roughly categorized as being climate-induced and human-induced. Methods for mapping human-induced CUG (H-CUG) are rare. In this paper, a new framework, known as Localized Spatial Association Analysis under Temporal Context (LSAA-TC), was proposed to explore H-CUG. Localized spatial association analysis (LSAA) was performed first to extract local spatial outliers (LSOs), or locations that differ significantly in urban greenness from those located in the neighborhood. LSOs were then analyzed under the temporal context to map their intertemporal variations known as spatiotemporal outliers. We applied LSAA-TC to mapping H-CUG in the Wuhan Metropolitan Area, China during 2000–2015 using the vegetation index from Moderate-resolution Imaging Spectroradiometer (MODIS) 13Q1 as the proxy for urban greenness. The computed H-CUG demonstrated apparent spatiotemporal patterns. The result is consistent with the fact that the traditional downtown area presents the lowest H-CUG, while it is found that the peripheral area in the circular belt within 14–20 km from the urban center demonstrates the most significant H-CUG. We conclude that LSAA-TC can be a widely applicable framework to understand H-CUG patterns and is a promising tool for informative urban planning.